D-ISN: TRACK 1: Collaborative Research: Disrupting Exploitation and Trafficking in Labor Supply Networks: Convergence of Behavioral and Decision Science to Design Interventions

这个非法供应网络（D-ISN）项目的破坏操作将有助于国家安全，繁荣，通过提高劳动力剥削和贩运的状态，经常发生在混乱的重建和恢复环境后，如飓风和流行病的理解和福利。 建筑供应链极易受到劳动力剥削和贩运的影响，特别是在技能较低的工人和日工中。 基于模型的决策框架将帮助决策者和个人通过设计更有效的干预措施，从社会角度预防和应对劳动剥削和贩运的情况。 该项目将为设计和部署大规模的干预目标和方案试验提供一条前进的道路。 持续的自然灾害（如COVID-19）对劳动力供应链的影响日益加剧，这使得决策框架能够以内在的弹性发展。 该项目将涉及早期职业学者，研究生，妇女，少数民族和多个机构，并将更广泛地促进行为科学和决策科学社区未来参与破坏非法供应网络。该研究项目将解决在复杂的劳动力供应生态系统中遇到的几个新颖和独特的功能，包括随机系统与部分信息，以捕捉的情况下，劳动者的状态和与他人的互动不能直接观察。此外，建筑工人的就业状况一般变化比较频繁。 拟议的框架将为政策制定者、监管机构和公司提供管理见解，指导他们如何利用有限的资源监测、打击和破坏劳动剥削和贩运。 这项研究有几个新颖和独特的方面，因为它努力捕捉相关复杂生态系统中遇到的以下关键特征：实证研究，随机多行为体网络模型，基于代理的仿真模型和批量强化学习。 该项目结合了行动研究和社区运作研究，以创建、测试和完善一个框架，用于设计和评估干预措施，其重点是补救非法的人类行为。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

This Disrupting Operations of Illicit Supply Networks (D-ISN) project will contribute to the national security, prosperity, and welfare by improving understanding of the states of laborer exploitation and trafficking that often occurs in the chaotic rebuild and recovery environment following natural disasters such as hurricanes and pandemics. Construction supply chains are extremely vulnerable to labor exploitation and trafficking, especially among workers with fewer skills and day laborers. The model-based decision framework will help policy makers and individuals prevent and respond to situations of labor exploitation and trafficking from a societal perspective by designing more efficacious interventions. The project will provide a path forward toward the design and deployment of a large-scale trial of interventional targets and programs. The exacerbating influences that ongoing natural disasters like COVID-19 have on labor supply chains allow the decision framework to be developed with built-in resilience. The project will involve early-career scholars, graduate students, women, minorities, and multiple institutions, and will more broadly facilitate future involvement of the behavioral science and decision science communities in the disruption of illicit supply networks. This research project will address several novel and unique features encountered in the complex labor supply ecosystem, including stochastic systems with partial information to capture the case where a laborer’s state and interactions with others cannot be directly observed. In addition, the employment status of construction laborers generally changes relatively frequently. The proposed framework will provide managerial insights to policy makers, regulators, and companies on how to monitor, combat, and disrupt labor exploitation and trafficking with limited resources. This research has several novel and unique aspects as it strives to capture the following critical features encountered in the relevant complex ecosystem: empirical research, stochastic multi-actor network models, agent-based simulation models, and batch reinforcement learning. The project combines action research and community operations research to create, test, and refine a framework for designing and evaluating interventions whose focus is to remediate illicit human behavior.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.